When you're starting out in data analysis, it's tempting to jump into Python, SQL, or R immediately. That’s what “everyone” seems to recommend, right?
But what if I told you that rushing into coding might actually slow you down?
As a former software engineer turned data analyst, here's my honest advice on how to start smarter, not harder.
1️⃣ Avoid Learning a Programming Language - at First
Yes, you read that right.
You don’t need to jump into Python, SQL, or R right away.
Why?
Because most real-world data analysis happens in what I call the "solo analyst scenario."
In this context:
- You work independently
- No one cares how you got the answer - only that you did
- Your code is rarely shared or reused
So instead of investing weeks learning syntax, focus on getting answers and insights quickly.
2️⃣ Use Microsoft Excel as Long as It Works for You
I used to look down on Excel. I thought:
"I'm a coder - I don’t need Excel."
Big mistake.
Over time, I realized Excel is one of the most powerful and flexible tools for data analysis.
Here’s why:
- Easy to learn, instantly useful
- Built-in charts, filters, and pivot tables
- Powerful diagnostic and visual analytics
- Perfect for most small-to-medium datasets
- Excel handles way more than most people think
👉 Check out this Excel resource
You might never hit its limits - especially if you have a decent laptop.
3️⃣ Use Excel Where It Makes Sense - Then Evolve
Just because Excel is powerful doesn’t mean it's always the best tool.
Use Excel until:
- Your data gets too large
- You need automation
- You want more reproducibility
- Or you're collaborating with a team
When that time comes, scale up to better tools - not before.
4️⃣ Learn SQL When You’re Ready
After Excel, SQL is the next logical step.
SQL is the language of databases. If your data lives in PostgreSQL, MySQL, Snowflake, or BigQuery, SQL is how you access it.
The good news?
- SQL is straightforward to learn
- You can integrate it directly into Excel
- It’s used everywhere - from startups to enterprise
SQL will unlock your ability to work with real-world data at scale.
5️⃣ Python in Excel Is a Game Changer
Did you know Microsoft now supports Python directly in Excel?
That means:
- You can scale past Excel’s limits without leaving your workbook
- You get access to NumPy, pandas, and Matplotlib - inside Excel
- 99% of your code can be reused in Jupyter or VS Code later
Eventually, you’ll outgrow Excel, and Python will be waiting - ready to level up your analysis.
✅ TL;DR: The Smart Path to Data Analysis
Here’s your action plan:
- Start with Excel
- Use it until you hit a real limitation
- Then learn SQL to work with databases
- Adopt Python (in Excel or outside) to automate and scale
By following this order, you’ll learn faster, solve real problems sooner, and stay focused on what matters most - generating insights from data.
💬 What’s Your Stack?
Are you still using Excel?
Have you moved to SQL or Python?
Let me know in the comments - I’d love to hear your journey!
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And remember - don’t waste your time learning tools before you need them.
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